Various computer-based approaches to retrieving images in response to image queries have been proposed. Content-based image retrieval (CBIR), for example, analyzes the content of an image in order to retrieve relevant images based on the respective content of images. A newer approach is content-free image retrieval (CFIR), which retrieves images based upon past user associations, regardless of the specific content of the images. Images can also be semantically annotated, according to another approach, so that the semantically annotated images can be organized and retrieved based on human-generated textual information.
These various approaches each have unique advantages, yet in different contexts also have distinct limitations. Accordingly, there is a need for a system that provides a user the option of selecting among and using different retrieval approaches depending on the particular context of a user's image query.
More fundamentally, a significant limitation of conventional image retrieval systems in many contexts is the typically limited image query capabilities of such systems. The limitations of conventional systems can significantly reduce the effectiveness of even sophisticated back-end systems. Thus, there also is a need for a system that provides the user greater flexibility in formulating image queries.